Biomedical Engineering, University of Southern California, Los Angeles, CA, United States.
The Johns Hopkins University Applied Physics Laboratory, Laurel, MD, United States.
J Neurosci Methods. 2022 Mar 15;370:109492. doi: 10.1016/j.jneumeth.2022.109492. Epub 2022 Jan 31.
Hippocampal memory prosthesis is defined as a closed-loop biomimetic system that can be used for restoration and enhancement of memory functions impaired in diseases or injuries. To build such a prosthesis, we have developed two types of input-output models, i.e., a multi-input multi-output (MIMO) model for predicting output spike trains based on input spikes, and a double-layer multi-resolution memory decoding (MD) model for classifying spatio-temporal patterns of spikes into memory categories. Both models can achieve high prediction accuracy using human hippocampal spikes data and can be used to derive electrical stimulation patterns to test the hippocampal memory prosthesis.
However, testing hippocampal memory prostheses in human epilepsy patients with such models has to be performed within a much shorter time window (48-72 h) due to clinical limitations. To solve this problem, we have developed parallelization strategies to decompose the overall model estimation task into multiple independent sub-tasks involving different outputs and cross-validation folds. These sub-tasks are then accomplished in parallel on different computer nodes to reduce model estimation time.
Implementing both parallel schemes with a high-performance computer cluster, we successfully reduced the computing time of model estimations from hundreds of hours to tens of hours.
We have tested the two parallel computing schemes for both MIMO and MD models with data collected from 11 human subjects. The performances of the parallel schemes are compared with the performance of the non-parallel scheme.
Such strategies allow us to complete the modeling procedure within the required time frame to further test input-output model-driven electrical stimulations for the hippocampal memory prosthesis. It has important implications to test the model-based DBS intraoperatively and developing clinically viable hippocampal memory prostheses.
海马记忆假体被定义为一种闭环仿生系统，可用于恢复和增强疾病或损伤导致的记忆功能障碍。为了构建这样的假体，我们开发了两种输入-输出模型，即基于输入尖峰预测输出尖峰序列的多输入多输出 (MIMO) 模型，以及用于将尖峰的时空模式分类为记忆类别的双层多分辨率记忆解码 (MD) 模型。这两种模型都可以使用人类海马尖峰数据实现高精度预测，并且可以用于导出电刺激模式来测试海马记忆假体。
然而，由于临床限制，使用这些模型在人类癫痫患者中测试海马记忆假体必须在更短的时间窗口（48-72 小时）内进行。为了解决这个问题，我们开发了并行化策略，将整体模型估计任务分解为多个涉及不同输出和交叉验证折叠的独立子任务。然后，这些子任务在不同的计算机节点上并行完成，以减少模型估计时间。
使用高性能计算机集群实现了这两种并行方案，我们成功地将模型估计的计算时间从数百小时缩短到数十小时。
我们使用从 11 名人类受试者收集的数据对 MIMO 和 MD 模型的两种并行计算方案进行了测试。比较了并行方案与非并行方案的性能。
这些策略使我们能够在所需的时间框架内完成建模过程，以便进一步测试基于输入-输出模型的电刺激对海马记忆假体的作用。这对于在手术中测试基于模型的 DBS 和开发临床可行的海马记忆假体具有重要意义。
